The fusion module is introduced to fuse the view features \(F_{VA}\in \mathbb {R}^{C\times N\times H\times W}\)  and channel features \(F_{CA}\in \mathbb {R}^{N\times C\times H\times W}\) . We reshape them to the same size and then concatenate them. To obtain highly informative features, the concatenated features \(F\in \mathbb {R}^{N\times 2C\times H\times W}\)  are fused by a fusion module, which consists of two \(1\times 1\)  convolution layers and two residual blocks (as shown in Fig. REF (e)). Inspired by the efficient sub-pixel convolutional layer [1]}, a pixel-shuffle layer followed by a \(3\times 3\)  convolution layer is used to upscale the fused features to the target resolution \(aH\times aW\)  (Fig. REF (f)).
